Overview

Dataset statistics

Number of variables14
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.5 KiB
Average record size in memory112.3 B

Variable types

NUM13
BOOL1

Warnings

tax is highly correlated with radHigh correlation
rad is highly correlated with taxHigh correlation
zn has 372 (73.5%) zeros Zeros

Reproduction

Analysis started2021-04-24 16:03:42.225442
Analysis finished2021-04-24 16:04:05.592812
Duration23.37 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

crim
Real number (ℝ≥0)

Distinct504
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.613523557
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:05.680612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02791
Q10.082045
median0.25651
Q33.6770825
95-th percentile15.78915
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.5950375

Descriptive statistics

Standard deviation8.601545105
Coefficient of variation (CV)2.380376098
Kurtosis37.13050913
Mean3.613523557
Median Absolute Deviation (MAD)0.22145
Skewness5.223148798
Sum1828.44292
Variance73.9865782
MonotocityNot monotonic
2021-04-24T17:04:05.805419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0150120.4%
 
14.333720.4%
 
0.5783410.2%
 
0.0612710.2%
 
0.0354810.2%
 
0.140310.2%
 
0.0370510.2%
 
0.9557710.2%
 
0.1174710.2%
 
0.0353710.2%
 
1.3879910.2%
 
10.23310.2%
 
0.5201410.2%
 
0.9761710.2%
 
0.290910.2%
 
0.1486610.2%
 
2.300410.2%
 
12.247210.2%
 
8.1517410.2%
 
0.3182710.2%
 
0.0615110.2%
 
0.0560210.2%
 
1.1308110.2%
 
1.4633610.2%
 
0.1208310.2%
 
Other values (479)47994.7%
 
ValueCountFrequency (%) 
0.0063210.2%
 
0.0090610.2%
 
0.0109610.2%
 
0.0130110.2%
 
0.0131110.2%
 
0.013610.2%
 
0.0138110.2%
 
0.0143210.2%
 
0.0143910.2%
 
0.0150120.4%
 
ValueCountFrequency (%) 
88.976210.2%
 
73.534110.2%
 
67.920810.2%
 
51.135810.2%
 
45.746110.2%
 
41.529210.2%
 
38.351810.2%
 
37.661910.2%
 
28.655810.2%
 
25.940610.2%
 

zn
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.36363636
Minimum0
Maximum100
Zeros372
Zeros (%)73.5%
Memory size4.1 KiB
2021-04-24T17:04:05.925674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.32245299
Coefficient of variation (CV)2.052375864
Kurtosis4.031510084
Mean11.36363636
Median Absolute Deviation (MAD)0
Skewness2.225666323
Sum5750
Variance543.9368137
MonotocityNot monotonic
2021-04-24T17:04:06.038377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
037273.5%
 
20214.2%
 
80153.0%
 
12.5102.0%
 
25102.0%
 
22102.0%
 
4071.4%
 
3061.2%
 
4561.2%
 
9051.0%
 
9540.8%
 
2140.8%
 
6040.8%
 
3340.8%
 
2830.6%
 
52.530.6%
 
7530.6%
 
3430.6%
 
3530.6%
 
7030.6%
 
5530.6%
 
82.520.4%
 
8520.4%
 
17.510.2%
 
10010.2%
 
ValueCountFrequency (%) 
037273.5%
 
12.5102.0%
 
17.510.2%
 
1810.2%
 
20214.2%
 
2140.8%
 
22102.0%
 
25102.0%
 
2830.6%
 
3061.2%
 
ValueCountFrequency (%) 
10010.2%
 
9540.8%
 
9051.0%
 
8520.4%
 
82.520.4%
 
80153.0%
 
7530.6%
 
7030.6%
 
6040.8%
 
5530.6%
 

indus
Real number (ℝ≥0)

Distinct76
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.13677866
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:06.160050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.860352941
Coefficient of variation (CV)0.6160087358
Kurtosis-1.233539601
Mean11.13677866
Median Absolute Deviation (MAD)6.32
Skewness0.2950215679
Sum5635.21
Variance47.06444247
MonotocityNot monotonic
2021-04-24T17:04:06.284715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
18.113226.1%
 
19.58305.9%
 
8.14224.3%
 
6.2183.6%
 
21.89153.0%
 
9.9122.4%
 
3.97122.4%
 
10.59112.2%
 
8.56112.2%
 
5.86102.0%
 
6.9191.8%
 
10.0191.8%
 
9.6981.6%
 
2.4681.6%
 
5.1981.6%
 
7.3881.6%
 
4.0571.4%
 
25.6571.4%
 
2.1871.4%
 
7.8771.4%
 
3.4461.2%
 
4.9361.2%
 
12.8361.2%
 
5.1361.2%
 
6.4151.0%
 
Other values (51)12624.9%
 
ValueCountFrequency (%) 
0.4610.2%
 
0.7410.2%
 
1.2110.2%
 
1.2210.2%
 
1.2520.4%
 
1.3210.2%
 
1.3810.2%
 
1.4720.4%
 
1.5240.8%
 
1.6920.4%
 
ValueCountFrequency (%) 
27.7451.0%
 
25.6571.4%
 
21.89153.0%
 
19.58305.9%
 
18.113226.1%
 
15.0430.6%
 
13.9251.0%
 
13.8940.8%
 
12.8361.2%
 
11.9351.0%
 

chas
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0
471 
1
 
35
ValueCountFrequency (%) 
047193.1%
 
1356.9%
 
2021-04-24T17:04:06.375474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

nox
Real number (ℝ≥0)

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5546950593
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:06.869152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.1158776757
Coefficient of variation (CV)0.2089033853
Kurtosis-0.06466713337
Mean0.5546950593
Median Absolute Deviation (MAD)0.0875
Skewness0.7293079225
Sum280.6757
Variance0.01342763572
MonotocityNot monotonic
2021-04-24T17:04:06.996811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.538234.5%
 
0.713183.6%
 
0.437173.4%
 
0.871163.2%
 
0.624153.0%
 
0.489153.0%
 
0.605142.8%
 
0.693142.8%
 
0.74132.6%
 
0.544122.4%
 
0.52112.2%
 
0.7112.2%
 
0.431102.0%
 
0.507102.0%
 
0.647102.0%
 
0.44891.8%
 
0.54791.8%
 
0.50481.6%
 
0.48881.6%
 
0.58581.6%
 
0.67981.6%
 
0.46481.6%
 
0.42881.6%
 
0.51581.6%
 
0.7781.6%
 
Other values (56)21542.5%
 
ValueCountFrequency (%) 
0.38510.2%
 
0.38910.2%
 
0.39220.4%
 
0.39410.2%
 
0.39820.4%
 
0.440.8%
 
0.40130.6%
 
0.40330.6%
 
0.40430.6%
 
0.40530.6%
 
ValueCountFrequency (%) 
0.871163.2%
 
0.7781.6%
 
0.74132.6%
 
0.71861.2%
 
0.713183.6%
 
0.7112.2%
 
0.693142.8%
 
0.67981.6%
 
0.67171.4%
 
0.66830.6%
 

rm
Real number (ℝ≥0)

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.284634387
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:07.124201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.7026171434
Coefficient of variation (CV)0.1117992074
Kurtosis1.891500366
Mean6.284634387
Median Absolute Deviation (MAD)0.3455
Skewness0.4036121333
Sum3180.025
Variance0.4936708502
MonotocityNot monotonic
2021-04-24T17:04:07.254847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.16730.6%
 
6.40530.6%
 
5.71330.6%
 
6.41730.6%
 
6.12730.6%
 
6.22930.6%
 
5.3920.4%
 
5.30420.4%
 
6.96820.4%
 
6.00920.4%
 
5.98320.4%
 
5.75720.4%
 
7.8220.4%
 
6.18520.4%
 
6.0320.4%
 
6.15220.4%
 
6.00420.4%
 
6.63520.4%
 
6.25120.4%
 
7.18520.4%
 
6.31520.4%
 
6.72820.4%
 
6.21120.4%
 
5.81320.4%
 
6.79420.4%
 
Other values (421)45088.9%
 
ValueCountFrequency (%) 
3.56110.2%
 
3.86310.2%
 
4.13820.4%
 
4.36810.2%
 
4.51910.2%
 
4.62810.2%
 
4.65210.2%
 
4.8810.2%
 
4.90310.2%
 
4.90610.2%
 
ValueCountFrequency (%) 
8.7810.2%
 
8.72510.2%
 
8.70410.2%
 
8.39810.2%
 
8.37510.2%
 
8.33710.2%
 
8.29710.2%
 
8.26610.2%
 
8.25910.2%
 
8.24710.2%
 

age
Real number (ℝ≥0)

Distinct356
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.57490119
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:07.385639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.725
Q145.025
median77.5
Q394.075
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.05

Descriptive statistics

Standard deviation28.14886141
Coefficient of variation (CV)0.410483441
Kurtosis-0.9677155942
Mean68.57490119
Median Absolute Deviation (MAD)19.55
Skewness-0.5989626399
Sum34698.9
Variance792.3583985
MonotocityNot monotonic
2021-04-24T17:04:07.514438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100438.5%
 
97.940.8%
 
9640.8%
 
95.440.8%
 
98.240.8%
 
87.940.8%
 
98.840.8%
 
97.430.6%
 
94.130.6%
 
96.230.6%
 
32.230.6%
 
95.630.6%
 
9730.6%
 
97.330.6%
 
8830.6%
 
36.630.6%
 
92.630.6%
 
21.430.6%
 
98.930.6%
 
76.530.6%
 
85.120.4%
 
82.520.4%
 
91.920.4%
 
8320.4%
 
59.720.4%
 
Other values (331)39077.1%
 
ValueCountFrequency (%) 
2.910.2%
 
610.2%
 
6.210.2%
 
6.510.2%
 
6.620.4%
 
6.810.2%
 
7.820.4%
 
8.410.2%
 
8.910.2%
 
9.810.2%
 
ValueCountFrequency (%) 
100438.5%
 
99.310.2%
 
99.110.2%
 
98.930.6%
 
98.840.8%
 
98.710.2%
 
98.510.2%
 
98.420.4%
 
98.320.4%
 
98.240.8%
 

dis
Real number (ℝ≥0)

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.795042688
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:07.646095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.105710127
Coefficient of variation (CV)0.5548580872
Kurtosis0.4879411222
Mean3.795042688
Median Absolute Deviation (MAD)1.29115
Skewness1.011780579
Sum1920.2916
Variance4.434015137
MonotocityNot monotonic
2021-04-24T17:04:07.765344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.495251.0%
 
5.720940.8%
 
5.287340.8%
 
6.814740.8%
 
5.400740.8%
 
7.827830.6%
 
3.945430.6%
 
7.30930.6%
 
5.491730.6%
 
6.479830.6%
 
6.062230.6%
 
4.812230.6%
 
5.116730.6%
 
7.317230.6%
 
5.415930.6%
 
6.49830.6%
 
3.651930.6%
 
4.721130.6%
 
6.336130.6%
 
4.967120.4%
 
1.951220.4%
 
4.354920.4%
 
4.566720.4%
 
3.671520.4%
 
2.777820.4%
 
Other values (387)43185.2%
 
ValueCountFrequency (%) 
1.129610.2%
 
1.13710.2%
 
1.169110.2%
 
1.174210.2%
 
1.178110.2%
 
1.202410.2%
 
1.285210.2%
 
1.316310.2%
 
1.321610.2%
 
1.332510.2%
 
ValueCountFrequency (%) 
12.126510.2%
 
10.710320.4%
 
10.585720.4%
 
9.222910.2%
 
9.220320.4%
 
9.187610.2%
 
9.089210.2%
 
8.906720.4%
 
8.792120.4%
 
8.696610.2%
 

rad
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.549407115
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:07.870062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.707259384
Coefficient of variation (CV)0.9118115166
Kurtosis-0.8672319936
Mean9.549407115
Median Absolute Deviation (MAD)2
Skewness1.004814648
Sum4832
Variance75.81636598
MonotocityNot monotonic
2021-04-24T17:04:07.966803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
2413226.1%
 
511522.7%
 
411021.7%
 
3387.5%
 
6265.1%
 
2244.7%
 
8244.7%
 
1204.0%
 
7173.4%
 
ValueCountFrequency (%) 
1204.0%
 
2244.7%
 
3387.5%
 
411021.7%
 
511522.7%
 
6265.1%
 
7173.4%
 
8244.7%
 
2413226.1%
 
ValueCountFrequency (%) 
2413226.1%
 
8244.7%
 
7173.4%
 
6265.1%
 
511522.7%
 
411021.7%
 
3387.5%
 
2244.7%
 
1204.0%
 

tax
Real number (ℝ≥0)

HIGH CORRELATION

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.2371542
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:08.079470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.5371161
Coefficient of variation (CV)0.4128411987
Kurtosis-1.142407992
Mean408.2371542
Median Absolute Deviation (MAD)73
Skewness0.6699559418
Sum206568
Variance28404.75949
MonotocityNot monotonic
2021-04-24T17:04:08.212522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
66613226.1%
 
307407.9%
 
403305.9%
 
437153.0%
 
304142.8%
 
264122.4%
 
398122.4%
 
384112.2%
 
277112.2%
 
330102.0%
 
224102.0%
 
43291.8%
 
23391.8%
 
27691.8%
 
39181.6%
 
29681.6%
 
28781.6%
 
19381.6%
 
31171.4%
 
18871.4%
 
27071.4%
 
22271.4%
 
30071.4%
 
28471.4%
 
32961.2%
 
Other values (41)10220.2%
 
ValueCountFrequency (%) 
18710.2%
 
18871.4%
 
19381.6%
 
19810.2%
 
21651.0%
 
22271.4%
 
22351.0%
 
224102.0%
 
22610.2%
 
23391.8%
 
ValueCountFrequency (%) 
71151.0%
 
66613226.1%
 
46910.2%
 
437153.0%
 
43291.8%
 
43030.6%
 
42210.2%
 
41120.4%
 
403305.9%
 
40220.4%
 

ptratio
Real number (ℝ≥0)

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.4555336
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:08.344171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.164945524
Coefficient of variation (CV)0.1173060379
Kurtosis-0.2850913833
Mean18.4555336
Median Absolute Deviation (MAD)1.15
Skewness-0.8023249269
Sum9338.5
Variance4.686989121
MonotocityNot monotonic
2021-04-24T17:04:08.469355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
20.214027.7%
 
14.7346.7%
 
21275.3%
 
17.8234.5%
 
19.2193.8%
 
17.4183.6%
 
18.6173.4%
 
19.1173.4%
 
16.6163.2%
 
18.4163.2%
 
21.2153.0%
 
15.2132.6%
 
13122.4%
 
17.9112.2%
 
20.9112.2%
 
18.791.8%
 
19.681.6%
 
19.781.6%
 
17.671.4%
 
16.461.2%
 
1651.0%
 
1851.0%
 
16.951.0%
 
16.151.0%
 
20.151.0%
 
Other values (21)5410.7%
 
ValueCountFrequency (%) 
12.630.6%
 
13122.4%
 
13.610.2%
 
14.410.2%
 
14.7346.7%
 
14.830.6%
 
14.940.8%
 
15.110.2%
 
15.2132.6%
 
15.330.6%
 
ValueCountFrequency (%) 
2220.4%
 
21.2153.0%
 
21.110.2%
 
21275.3%
 
20.9112.2%
 
20.214027.7%
 
20.151.0%
 
19.781.6%
 
19.681.6%
 
19.2193.8%
 

b
Real number (ℝ≥0)

Distinct357
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.6740316
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:08.598011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.59
Q1375.3775
median391.44
Q3396.225
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.8475

Descriptive statistics

Standard deviation91.29486438
Coefficient of variation (CV)0.255961624
Kurtosis7.226817549
Mean356.6740316
Median Absolute Deviation (MAD)5.46
Skewness-2.890373712
Sum180477.06
Variance8334.752263
MonotocityNot monotonic
2021-04-24T17:04:08.727678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
396.912123.9%
 
395.2430.6%
 
393.7430.6%
 
394.1220.4%
 
395.5620.4%
 
390.9420.4%
 
388.4520.4%
 
393.2320.4%
 
396.2120.4%
 
393.3720.4%
 
391.3420.4%
 
396.0620.4%
 
392.820.4%
 
395.6320.4%
 
389.7120.4%
 
395.1120.4%
 
394.7220.4%
 
377.0720.4%
 
341.620.4%
 
393.4520.4%
 
395.5820.4%
 
395.6920.4%
 
376.1420.4%
 
374.7120.4%
 
392.7820.4%
 
Other values (332)33566.2%
 
ValueCountFrequency (%) 
0.3210.2%
 
2.5210.2%
 
2.610.2%
 
3.510.2%
 
3.6510.2%
 
6.6810.2%
 
7.6810.2%
 
9.3210.2%
 
10.4810.2%
 
16.4510.2%
 
ValueCountFrequency (%) 
396.912123.9%
 
396.4210.2%
 
396.3310.2%
 
396.310.2%
 
396.2810.2%
 
396.2410.2%
 
396.2310.2%
 
396.2120.4%
 
396.1410.2%
 
396.0620.4%
 

lstat
Real number (ℝ≥0)

Distinct455
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.65306324
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:08.867305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7075
Q16.95
median11.36
Q316.955
95-th percentile26.8075
Maximum37.97
Range36.24
Interquartile range (IQR)10.005

Descriptive statistics

Standard deviation7.141061511
Coefficient of variation (CV)0.5643741263
Kurtosis0.4932395174
Mean12.65306324
Median Absolute Deviation (MAD)4.795
Skewness0.9064600936
Sum6402.45
Variance50.99475951
MonotocityNot monotonic
2021-04-24T17:04:08.988979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8.0530.6%
 
6.3630.6%
 
18.1330.6%
 
14.130.6%
 
7.7930.6%
 
18.4620.4%
 
9.9720.4%
 
5.3320.4%
 
10.4520.4%
 
6.7220.4%
 
21.3220.4%
 
12.6720.4%
 
3.5320.4%
 
17.620.4%
 
5.520.4%
 
7.620.4%
 
12.0320.4%
 
12.4320.4%
 
13.2720.4%
 
5.4920.4%
 
14.8120.4%
 
6.5820.4%
 
13.4420.4%
 
17.2720.4%
 
1320.4%
 
Other values (430)45189.1%
 
ValueCountFrequency (%) 
1.7310.2%
 
1.9210.2%
 
1.9810.2%
 
2.4710.2%
 
2.8710.2%
 
2.8810.2%
 
2.9410.2%
 
2.9610.2%
 
2.9710.2%
 
2.9810.2%
 
ValueCountFrequency (%) 
37.9710.2%
 
36.9810.2%
 
34.7710.2%
 
34.4110.2%
 
34.3710.2%
 
34.0210.2%
 
31.9910.2%
 
30.8120.4%
 
30.6310.2%
 
30.6210.2%
 

medv
Real number (ℝ≥0)

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.53280632
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-04-24T17:04:09.128606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.197104087
Coefficient of variation (CV)0.408165053
Kurtosis1.495196944
Mean22.53280632
Median Absolute Deviation (MAD)4
Skewness1.108098408
Sum11401.6
Variance84.58672359
MonotocityNot monotonic
2021-04-24T17:04:09.256784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50163.2%
 
2581.6%
 
21.771.4%
 
2271.4%
 
23.171.4%
 
20.661.2%
 
19.461.2%
 
13.851.0%
 
22.651.0%
 
21.251.0%
 
19.351.0%
 
22.251.0%
 
23.951.0%
 
20.151.0%
 
2051.0%
 
19.651.0%
 
17.851.0%
 
21.451.0%
 
15.651.0%
 
18.940.8%
 
20.340.8%
 
19.540.8%
 
23.240.8%
 
23.840.8%
 
18.540.8%
 
Other values (204)36572.1%
 
ValueCountFrequency (%) 
520.4%
 
5.610.2%
 
6.310.2%
 
720.4%
 
7.230.6%
 
7.410.2%
 
7.510.2%
 
8.110.2%
 
8.320.4%
 
8.420.4%
 
ValueCountFrequency (%) 
50163.2%
 
48.810.2%
 
48.510.2%
 
48.310.2%
 
46.710.2%
 
4610.2%
 
45.410.2%
 
44.810.2%
 
4410.2%
 
43.810.2%
 

Interactions

2021-04-24T17:03:45.311277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:45.419013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:45.525701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:45.631454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:45.733178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:45.837900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:45.943616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:46.058006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:46.168709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:46.289387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:46.401090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:46.534733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:46.653414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:46.763632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-24T17:03:47.009506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:47.131236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:47.241104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-24T17:03:47.458243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:47.567041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:47.682225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-24T17:03:49.743888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-24T17:03:53.082664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-24T17:03:58.769349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:03:58.890027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-24T17:03:59.219754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-24T17:04:00.932802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:01.058497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:01.186156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:01.317804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:01.446428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:01.570110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:01.687976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:01.822620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:01.954348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:02.075026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:02.188753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:02.305441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:02.422845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:02.537539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:02.654346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:02.772031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:02.887754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:03.004163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:03.120850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:03.233123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:03.360782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:03.484452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:03.608091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:03.717828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:03.831524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:03.947215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:04.060880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:04.176570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:04.291300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:04.409012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:04.523705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:04.636400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:04.741121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:04.859803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:04.971504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-24T17:04:09.379470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-24T17:04:09.605865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-24T17:04:09.833280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-24T17:04:10.059696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-24T17:04:05.180914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-24T17:04:05.483106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

crimzninduschasnoxrmagedisradtaxptratioblstatmedv
00.0063218.02.3100.5386.57565.24.0900129615.3396.904.9824.0
10.027310.07.0700.4696.42178.94.9671224217.8396.909.1421.6
20.027290.07.0700.4697.18561.14.9671224217.8392.834.0334.7
30.032370.02.1800.4586.99845.86.0622322218.7394.632.9433.4
40.069050.02.1800.4587.14754.26.0622322218.7396.905.3336.2
50.029850.02.1800.4586.43058.76.0622322218.7394.125.2128.7
60.0882912.57.8700.5246.01266.65.5605531115.2395.6012.4322.9
70.1445512.57.8700.5246.17296.15.9505531115.2396.9019.1527.1
80.2112412.57.8700.5245.631100.06.0821531115.2386.6329.9316.5
90.1700412.57.8700.5246.00485.96.5921531115.2386.7117.1018.9

Last rows

crimzninduschasnoxrmagedisradtaxptratioblstatmedv
4960.289600.09.6900.5855.39072.92.7986639119.2396.9021.1419.7
4970.268380.09.6900.5855.79470.62.8927639119.2396.9014.1018.3
4980.239120.09.6900.5856.01965.32.4091639119.2396.9012.9221.2
4990.177830.09.6900.5855.56973.52.3999639119.2395.7715.1017.5
5000.224380.09.6900.5856.02779.72.4982639119.2396.9014.3316.8
5010.062630.011.9300.5736.59369.12.4786127321.0391.999.6722.4
5020.045270.011.9300.5736.12076.72.2875127321.0396.909.0820.6
5030.060760.011.9300.5736.97691.02.1675127321.0396.905.6423.9
5040.109590.011.9300.5736.79489.32.3889127321.0393.456.4822.0
5050.047410.011.9300.5736.03080.82.5050127321.0396.907.8811.9